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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,132 +1,207 @@ import os import torch import torch.nn as nn import torch.nn.functional as F from torchvision import datasets, transforms from torchvision.transforms import functional as TF import matplotlib.pyplot as plt import tkinter as tk from PIL import Image, ImageDraw, ImageOps import warnings # 1. Set up device (CPU or local GPU) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # 2. Define your model class MyModel(nn.Module): def __init__(self, num_classes=10): super(MyModel, self).__init__() # fixed super() call # Feature extractor self.features = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, padding=0), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(32, 64, kernel_size=3, padding=0), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) # Classifier self.classifier = nn.Sequential( nn.Flatten(), nn.Linear(64 * 5 * 5, 64), nn.ReLU(inplace=True), nn.Linear(64, num_classes) ) def forward(self, x): x = self.features(x) x = self.classifier(x) return x # 3. Instantiate and move model to device model = MyModel(num_classes=10).to(device) # 4. (Re)define your loss, optimizer, and hyperparameters loss_fn = nn.CrossEntropyLoss() learning_rate = 0.1 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) epochs = 20 # 5. Training and testing loops (now using `device`) def train(dataloader, model, loss_fn, optimizer): model.train() size = len(dataloader.dataset) num_batches = len(dataloader) train_loss = 0.0 train_acc = 0.0 for inputs, labels in dataloader: inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() pred = model(inputs) loss = loss_fn(pred, labels) loss.backward() optimizer.step() train_loss += loss.item() train_acc += (pred.argmax(1) == labels).float().sum().item() train_loss /= num_batches train_acc /= size return train_acc, train_loss def test(dataloader, model, loss_fn): model.eval() size = len(dataloader.dataset) num_batches = len(dataloader) test_loss = 0.0 test_acc = 0.0 with torch.no_grad(): for inputs, labels in dataloader: inputs, labels = inputs.to(device), labels.to(device) pred = model(inputs) loss = loss_fn(pred, labels) test_loss += loss.item() test_acc += (pred.argmax(1) == labels).float().sum().item() test_loss /= num_batches test_acc /= size return test_acc, test_loss # 6. Initial all the parameters model_path = "learn_mnist.pth" if os.path.exists(model_path): model.load_state_dict(torch.load(model_path, map_location=device, weights_only=True)) model.eval() print("模型已加载，无需重新训练。") else: # 数据准备 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = datasets.MNIST('', train=True, download=True, transform=transform) test_dataset = datasets.MNIST('', train=False, transform=transform) train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=256, shuffle=True) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=1000, shuffle=False) train_acc_history = [] train_loss_history = [] test_acc_history = [] test_loss_history = [] # 7. Run for epoch in range(1, epochs + 1): t_acc, t_loss = train(train_dl, model, loss_fn, optimizer) v_acc, v_loss = test(test_dl, model, loss_fn) train_acc_history.append(t_acc) train_loss_history.append(t_loss) test_acc_history.append(v_acc) test_loss_history.append(v_loss) print(f"Epoch {epoch:2d}: " f"Train Acc {t_acc*100:5.1f}% Loss {t_loss:.3f} | " f"Val Acc {v_acc*100:5.1f}% Loss {v_loss:.3f}") print("Training complete.") torch.save(model.state_dict(), model_path) print("模型已训练并保存。") # 7. Plotting warnings.filterwarnings("ignore") plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams['figure.dpi'] = 100 epochs_range = range(1, epochs + 1) plt.figure(figsize=(12, 3)) plt.subplot(1, 2, 1) plt.plot(epochs_range, train_acc_history, label='Training Acc') plt.plot(epochs_range, test_acc_history, label='Validation Acc') plt.legend(loc='lower right') plt.title('Accuracy') plt.subplot(1, 2, 2) plt.plot(epochs_range, train_loss_history, label='Training Loss') plt.plot(epochs_range, test_loss_history, label='Validation Loss') plt.legend(loc='upper right') plt.title('Loss') plt.show() # 8. Tkinter def predict_digit(img): img = img.resize((28, 28)).convert('L') img = ImageOps.invert(img) img = TF.to_tensor(img).unsqueeze(0) img = TF.normalize(img, [0.1307], [0.3081]) img = img.to(device) with torch.no_grad(): output = model(img) pred = torch.argmax(output, dim=1) return pred.item() # 手写板类 class App(tk.Tk): def __init__(self): super().__init__() self.title("手写数字识别") self.canvas = tk.Canvas(self, width=200, height=200, bg="white") self.canvas.pack() self.image = Image.new("RGB", (200, 200), "white") self.draw = ImageDraw.Draw(self.image) self.canvas.bind("<B1-Motion>", self.paint) tk.Button(self, text="识别", command=self.recognize).pack() tk.Button(self, text="清除", command=self.clear).pack() self.result = tk.Label(self, text="", font=("Helvetica", 20)) self.result.pack() def paint(self, event): x, y = event.x, event.y r = 8 self.canvas.create_oval(x - r, y - r, x + r, y + r, fill='black') self.draw.ellipse([x - r, y - r, x + r, y + r], fill='black') def recognize(self): digit = predict_digit(self.image) self.result.config(text=f"识别结果：{digit}") def clear(self): self.canvas.delete("all") self.draw.rectangle([0, 0, 200, 200], fill="white") self.result.config(text="") # 启动GUI App().mainloop() -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,132 @@ import torch import torch.nn as nn import torch.nn.functional as F class MyModel(nn.Module): def __init__(self, num_classes=10): super(SequentialModel, self).__init__() # 特征提取 self.features = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, padding=0), # 输入通道数为1，输出通道数为32，卷积核大小为3x3 nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # 池化层，池化核大小为2x2 nn.Conv2d(32, 64, kernel_size=3, padding=0), # 输入通道数为32，输出通道数为64，卷积核大小为3x3 nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # 池化层，池化核大小为2x2 ) # 分类网络 self.classifier = nn.Sequential( nn.Flatten(), # 将特征图展平成一维向量 nn.Linear(64 * 5 * 5, 64), # 全连接层，输入节点数64*5*5，输出节点数64 nn.ReLU(inplace=True), nn.Linear(64, num_classes) # 全连接层，输入节点数64，输出节点数num_classes ) def forward(self, x): x = self.features(x) x = self.classifier(x) return x def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集的大小，一共60000张图片 num_batches = len(dataloader) # 批次数目，1875（60000/32） train_loss, train_acc = 0, 0 # 初始化训练损失和正确率 for X, y in dataloader: # 获取图片及其标签 X, y = X.to(choose_device()), y.to(choose_device()) # 如果可以使用GPU的话，将数据移到GPU上，否则在CPU上 # 计算预测误差 pred = model(X) # 网络输出 loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失，注意二者顺序 # 反向传播 optimizer.zero_grad() # grad属性归零 loss.backward() # 反向传播 optimizer.step() # 梯度更新 # 记录acc与loss train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() # 这行代码很漂亮且很关键，下面详细分析 train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss def test(dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小，一共10000张图片 num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，向上取整） test_loss, test_acc = 0, 0 # 当不进行训练时，停止梯度更新，节省计算内存消耗 with torch.no_grad(): for imgs, target in dataloader: imgs, target = imgs.to(choose_device()), target.to(choose_device()) # 计算loss target_pred = model(imgs) loss = loss_fn(target_pred, target) test_loss += loss.item() test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item() test_acc /= size test_loss /= num_batches return test_acc, test_loss # 设置超参数 epochs = 20 loss_function = nn.CrossEntropyLoss() learning_rate = 0.1 opt = torch.optim.SGD(model.parameters(), lr=learning_rate) train_loss = [] train_acc = [] test_loss = [] test_acc = [] # 开始训练 for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_function, opt) model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_function) train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss)) print('Done') import matplotlib.pyplot as plt # 隐藏警告 import warnings warnings.filterwarnings("ignore") # 忽略警告信息 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.rcParams['figure.dpi'] = 100 # 分辨率 epochs_range = range(epochs) plt.figure(figsize=(12, 3)) plt.subplot(1, 2, 1) plt.plot(epochs_range, train_acc, label='Training Accuracy') plt.plot(epochs_range, test_acc, label='Test Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.subplot(1, 2, 2) plt.plot(epochs_range, train_loss, label='Training Loss') plt.plot(epochs_range, test_loss, label='Test Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show() File renamed without changes. -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,140 @@ import os import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torchvision.transforms import functional as TF import tkinter as tk from PIL import Image, ImageDraw, ImageOps import numpy as np # 设备设置 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_path = "lenet5_mnist.pth" # 定义LeNet-5模型 class LeNet5(nn.Module): def __init__(self): super(LeNet5, self).__init__() self.conv1 = nn.Conv2d(1,6,5) self.conv2 = nn.Conv2d(6,16,5) self.fc1 = nn.Linear(256,120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84,10) def forward(self, x): x = F.max_pool2d(F.relu(self.conv1(x)), 2) x = F.max_pool2d(F.relu(self.conv2(x)), 2) x = x.view(-1, 256) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x # 实例化模型 model = LeNet5().to(device) # 如果模型文件存在，则加载 if os.path.exists(model_path): model.load_state_dict(torch.load(model_path, map_location=device)) model.eval() print("模型已加载，无需重新训练。") else: # 数据准备 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = datasets.MNIST('', train=True, download=True, transform=transform) test_dataset = datasets.MNIST('', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=256, shuffle=True) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1000, shuffle=False) # 优化器与损失函数 optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() # 训练模型 print("开始训练模型...") epochs = 5 for epoch in range(epochs): running_loss = 0.0 model.train() for inputs, labels in train_loader: inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f'Epoch {epoch + 1}, Loss: {running_loss / len(train_loader)}') # 保存模型 torch.save(model.state_dict(), model_path) print("模型已训练并保存。") # 测试准确率 model.eval() correct = 0 total = 0 with torch.no_grad(): for images, labels in test_loader: images, labels = images.to(device), labels.to(device) outputs = model(images) _, predicted = torch.max(outputs, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print(f'测试准确率: {100 * correct / total:.2f}%') # 可视化手写识别（Tkinter） def predict_digit(img): img = img.resize((28, 28)).convert('L') img = ImageOps.invert(img) img = TF.to_tensor(img).unsqueeze(0) img = TF.normalize(img, [0.1307], [0.3081]) img = img.to(device) with torch.no_grad(): output = model(img) pred = torch.argmax(output, dim=1) return pred.item() # 手写板类 class App(tk.Tk): def __init__(self): super().__init__() self.title("手写数字识别") self.canvas = tk.Canvas(self, width=200, height=200, bg="white") self.canvas.pack() self.image = Image.new("RGB", (200, 200), "white") self.draw = ImageDraw.Draw(self.image) self.canvas.bind("<B1-Motion>", self.paint) tk.Button(self, text="识别", command=self.recognize).pack() tk.Button(self, text="清除", command=self.clear).pack() self.result = tk.Label(self, text="", font=("Helvetica", 20)) self.result.pack() def paint(self, event): x, y = event.x, event.y r = 8 self.canvas.create_oval(x - r, y - r, x + r, y + r, fill='black') self.draw.ellipse([x - r, y - r, x + r, y + r], fill='black') def recognize(self): digit = predict_digit(self.image) self.result.config(text=f"识别结果：{digit}") def clear(self): self.canvas.delete("all") self.draw.rectangle([0, 0, 200, 200], fill="white") self.result.config(text="") # 启动GUI App().mainloop()